SoroushVahidi/scidocs huggingface dataset:
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
(2015)

Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study [30] revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call “fooling images” (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.
Analysis of classifiers’ robustness to adversarial perturbations
(2017)

The goal of this paper is to analyze the intriguing instability of classifiers to adversarial perturbations (Szegedy et al., in: International conference on learning representations (ICLR), 2014). We provide a theoretical framework for analyzing the robustness of classifiers to adversarial perturbations, and show fundamental upper bounds on the robustness of classifiers. Specifically, we establish a general upper bound on the robustness of classifiers to adversarial perturbations, and then illustrate the obtained upper bound on two practical classes of classifiers, namely the linear and quadratic classifiers. In both cases, our upper bound depends on a distinguishability measure that captures the notion of difficulty of the classification task. Our results for both classes imply that in tasks involving small distinguishability, no classifier in the considered set will be robust to adversarial perturbations, even if a good accuracy is achieved. Our theoretical framework moreover suggests that the phenomenon of adversarial instability is due to the low flexibility of classifiers, compared to the difficulty of the classification task (captured mathematically by the distinguishability measure). We further show the existence of a clear distinction between the robustness of a classifier to random noise and its robustness to adversarial perturbations. Specifically, the former is shown to be larger than the latter by a factor that is proportional to $$\sqrt{d}$$ d (with d being the signal dimension) for linear classifiers. This result gives a theoretical explanation for the discrepancy between the two robustness properties in high dimensional problems, which was empirically observed by Szegedy et al. in the context of neural networks. We finally show experimental results on controlled and real-world data that confirm the theoretical analysis and extend its spirit to more complex classification schemes.
The evolution of Crew Resource Management training in commercial aviation.
(1999)

In this study, we describe changes in the nature of Crew Resource Management (CRM) training in commercial aviation, including its shift from cockpit to crew resource management. Validation of the impact of CRM is discussed. Limitations of CRM, including lack of cross-cultural generality are considered. An overarching framework that stresses error management to increase acceptance of CRM concepts is presented. The error management approach defines behavioral strategies taught in CRM as error countermeasures that are employed to avoid error, to trap errors committed, and to mitigate the consequences of error.
Understanding Types of Cyberbullying in an Anonymous Messaging Application
(2018)

The possibility of anonymity and lack of effective ways to identify inappropriate messages have resulted in a significant amount of online interaction data that attempt to harass, bully, or offend the recipient. In this work, we perform a preliminary linguistic study on messages exchanged using one such popular web/smartphone application—Sarahah, that allows friends to exchange messages anonymously. Since messages exchanged via Sarahah are private, we collect them when the recipient shares it on Twitter. We then perform an analysis of the different kinds of messages exchanged through this application. Our linguistic analysis reveals that a significant number of these messages (∼ 20%) include inappropriate, hurtful, or profane language intended to embarrass, offend, or bully the recipient. Our analysis helps in understanding the different ways in which anonymous message exchange platforms are used and the different types of bullying present in such exchanges.
Transient response and dynamic power dissipation comparison of various Dickson charge pump configurations based on charge transfer switches
(2015)

Dickson charge pump is a switches-capacitor network providing a voltage gain and having open-circuit resistance dependent on the topology frequency and the size of storage capacitors. In this paper, the current status of research and development in the field of Dickson charge pump is extensively reviewed. Dickson charge pump is inductor-less DC to DC converter which uses a capacitor for its energy storage. The aim of this paper is to compare various techniques of Dickson charge pump currently explored in industry and academia. The parameters especially efficiency and voltage are compared. We have also discussed and evaluated the optimised criteria of capacitor sizing according to clock signals provided such that the charge pump performance could be properly optimised. We have simulated the transient response of DCP and provided the comprehensive study.
From GPS traces to a routable road map
(2009)

This paper presents a method for automatically converting raw GPS traces from everyday vehicles into a routable road network. The method begins by smoothing raw GPS traces using a novel aggregation technique. This technique pulls together traces that belong on the same road in response to simulated potential energy wells created around each trace. After the traces are moved in response to the potential fields, they tend to coalesce into smooth paths. To help adjust the parameters of the constituent potential fields, we present a theoretical analysis of the behavior of our algorithm on a few different road configurations. With the resulting smooth traces, we apply a custom clustering algorithm to create a graph of nodes and edges representing the road network. We show how this network can be used to plan reasonable driving routes, much like consumer-oriented mapping Web sites. We demonstrate our algorithms using real GPS data collected on public roads, and we evaluate the effectiveness of our approach on public roads, and we evaluate the effectiveness of our approach by comparing the route planning results suggested by our generated graph to a commercial route planner.
Modeling and control of a hexacopter with a rotating seesaw
(2016)

This paper presents a novel multicomputer type with a unique structure. This structure allows decoupling angular motion from translational motion along one of the body axes, and by that, five of the body degrees of freedom (DoF) can be controlled independently. The proposed aerial vehicle is a hexacopter with six propellers, such that four propellers are attached to the vehicle body and the other two attached to a seesaw. The seesaw is free to rotate around a single axis that is fixed in the body frame. Compared to a standard hexacopter, this unique design improves the maneuverability of the aerial vehicle, without the price of additional actuators. As opposed to tilting-rotor aerial-vehicle, in the proposed design all actuators generate lift. The paper describes the dynamical model of the novel vehicle and suggests a nonlinear controller to track desired trajectories along five of its DoF. The performances of the design are demonstrated numerically.
Peer victimization, psychosocial adjustment, and physical activity in overweight and at-risk-for-overweight youth.
(2007)

OBJECTIVE
To examine the relationship between peer victimization and child and parent reports of psychosocial adjustment and physical activity in a clinical sample of at-risk-for-overweight and overweight children and adolescents.


METHODS
The Schwartz Peer Victimization Scale, Children's Depression Inventory-Short Form, Multidimensional Anxiety Scale for Children, Social Physique Anxiety Scale, PACE+ Adolescent Physical Activity Measure, and Asher Loneliness Scale were administered to 92 children and adolescents (54 females) aged 8-18 years. The youth's parent/guardian completed the Child Behavior Checklist.


RESULTS
Peer victimization was positively related to child-reported depression, anxiety, social physique anxiety, and loneliness, and parent-reported internalizing and externalizing symptoms. Peer victimization was negatively related to physical activity. Depressive symptoms and loneliness mediated the relations between peer victimization and physical activity.


CONCLUSION
Recognition of the magnitude of the problem and the means of evaluating for peer victimization is important for clinicians who work with overweight youth. Assessing peer experiences may assist in understanding rates of physical activity and/or past nonadherence to clinician recommendations.
Investigations on Knowledge Base Embedding for Relation Prediction and Extraction
(2018)

We report an evaluation of the effectiveness of the existing knowledge base embedding models for relation prediction and for relation extraction on a wide range of benchmarks. We also describe a new benchmark, which is much larger and complex than previous ones, which we introduce to help validate the effectiveness of both tasks. The results demonstrate that knowledge base embedding models are generally effective for relation prediction but unable to give improvements for the state-of-art neural relation extraction model with the existing strategies, while pointing limitations of existing methods.
Agents on Stage: Advancing the State of the Art of AI
(1995)

Intelligent computer agents are both the original goal and the ultimate goal of artificial intelligence research. In striving toward that goal, our community has followed a practical research strategy of "divide-and-conquer," with different sub-communities attacking important component functions of intelligence, such as planning, search, knowledge representation, vision, and natural language. This strategy has been almost too successful, yielding both challenging theoretical problems that have come to dominate the inquiries of many researchers and a spate of practical techniques that have enabled other researchers to move into profitable commercial enterprises. While each of these pursuits is worthy in its own right, together they have had the unfortunate sideeffect of fragmenting the field and almost completely diverting it from efforts to build intelligent agents.
An Effective CU Size Decision Method for HEVC Encoders
(2013)

The emerging high efficiency video coding standard (HEVC) adopts the quadtree-structured coding unit (CU). Each CU allows recursive splitting into four equal sub-CUs. At each depth level (CU size), the test model of HEVC (HM) performs motion estimation (ME) with different sizes including 2N × 2N, 2N × N, N × 2N and N × N. ME process in HM is performed using all the possible depth levels and prediction modes to find the one with the least rate distortion (RD) cost using Lagrange multiplier. This achieves the highest coding efficiency but requires a very high computational complexity. In this paper, we propose a fast CU size decision algorithm for HM. Since the optimal depth level is highly content-dependent, it is not efficient to use all levels. We can determine CU depth range (including the minimum depth level and the maximum depth level) and skip some specific depth levels rarely used in the previous frame and neighboring CUs. Besides, the proposed algorithm also introduces early termination methods based on motion homogeneity checking, RD cost checking and SKIP mode checking to skip ME on unnecessary CU sizes. Experimental results demonstrate that the proposed algorithm can significantly reduce computational complexity while maintaining almost the same RD performance as the original HEVC encoder.
Vectorization vs. compilation in query execution
(2011)

Compiling database queries into executable (sub-) programs provides substantial benefits comparing to traditional interpreted execution. Many of these benefits, such as reduced interpretation overhead, better instruction code locality, and providing opportunities to use SIMD instructions, have previously been provided by redesigning query processors to use a vectorized execution model. In this paper, we try to shed light on the question of how state-of-the-art compilation strategies relate to vectorized execution for analytical database workloads on modern CPUs. For this purpose, we carefully investigate the behavior of vectorized and compiled strategies inside the Ingres VectorWise database system in three use cases: Project, Select and Hash Join. One of the findings is that compilation should always be combined with block-wise query execution. Another contribution is identifying three cases where "loop-compilation" strategies are inferior to vectorized execution. As such, a careful merging of these two strategies is proposed for optimal performance: either by incorporating vectorized execution principles into compiled query plans or using query compilation to create building blocks for vectorized processing.
"PASS" principles for predictable bone regeneration.
(2006)

Guided bone regeneration is a well-established technique used for augmentation of deficient alveolar ridges. Predictable regeneration requires both a high level of technical skill and a thorough understanding of underlying principles of wound healing. This article describes the 4 major biologic principles (i.e., PASS) necessary for predictable bone regeneration: primary wound closure to ensure undisturbed and uninterrupted wound healing, angiogenesis to provide necessary blood supply and undifferentiated mesenchymal cells, space maintenance/creation to facilitate adequate space for bone ingrowth, and stability of wound and implant to induce blood clot formation and uneventful healing events. In addition, a novel flap design and clinical cases using this principle are presented.
Expression Profiling of Developing Zebrafish Retinal Cells.
(2016)

During retinal development, a variety of different types of neurons are produced. Understanding how each of these types of retinal nerve cells is generated is important from a developmental biology perspective. It is equally important if one is interested in how to regenerate cells after an injury or a disease. To gain more insight into how retinal neurons develop in the zebrafish, we performed single-cell mRNA profiling and in situ hybridizations (ISHs) on retinal sections and whole-mount zebrafish. Through the series of ISHs, designed and performed solely by undergraduate students in the laboratory, we were able to retrospectively identify our single-cell mRNA profiles as most likely coming from developing amacrine cells. Further analysis of these profiles will reveal genes that can be mutated using genome editing techniques. Together these studies increase our knowledge of the genes driving development of different cell types in the zebrafish retina.
Context and behavioral processes in extinction.
(2004)

This article provides a selective review and integration of the behavioral literature on Pavlovian extinction. The first part reviews evidence that extinction does not destroy the original learning, but instead generates new learning that is especially context-dependent. The second part examines insights provided by research on several related behavioral phenomena (the interference paradigms, conditioned inhibition, and inhibition despite reinforcement). The final part examines four potential causes of extinction: the discrimination of a new reinforcement rate, generalization decrement, response inhibition, and violation of a reinforcer expectation. The data are consistent with behavioral models that emphasize the role of generalization decrement and expectation violation, but would be more so if those models were expanded to better accommodate the finding that extinction involves a context-modulated form of inhibitory learning.
Forecasting seasonal time series with computational intelligence: On recent methods and the potential of their combinations
(2013)

Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on seasonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series. 2012 Elsevier Ltd. All rights reserved.
A Wide-Range Model for Metal-Oxide Surge Arrester
(2018)

This paper presents an electric model of a metal-oxide surge arrester (MOSA). The proposed electric model accurately represents the MOSA in a wide range of frequencies and amplitudes. The model was developed and validated based on MOSA electrical behavior in each one of the three operating regions of the zinc-oxide (ZnO) surge arresters, and in a database composed of voltage and current waveforms measured from tests performed in 12 ZnO varistors having different physical dimensions and electrical characteristics—from five different manufacturers. These varistors were subjected to different voltage levels in the low current region, and multilevel amplitude of switching current impulses (30/60 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s), lightning current impulses (8/20 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s), high current impulses (4/10 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s), and fast-front current impulses (1.5/26 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s and 3/6 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s) encompass the three regions of operation and a wide range of frequencies and amplitudes. The results provided by the MOSA wide-range (MWR) model were compared with those obtained in the laboratory. The MWR model has shown good agreement in terms of waveform, peak value, and absorbed energy for the evaluated cases.
Perceptually based brush strokes for nonphotorealistic visualization
(2004)

An important problem in the area of computer graphics is the visualization of large, complex information spaces. Datasets of this type have grown rapidly in recent years, both in number and in size. Images of the data stored in these collections must support rapid and accurate exploration and analysis. This article presents a method for constructing visualizations that are both effective and aesthetic. Our approach uses techniques from master paintings and human perception to visualize a multidimensional dataset. Individual data elements are drawn with one or more brush strokes that vary their appearance to represent the element's attribute values. The result is a nonphotorealistic visualization of information stored in the dataset. Our research extends existing glyph-based and nonphotorealistic techniques by applying perceptual guidelines to build an effective representation of the underlying data. The nonphotorealistic properties the strokes employ are selected from studies of the history and theory of Impressionist art. We show that these properties are similar to visual features that are detected by the low-level human visual system. This correspondence allows us to manage the strokes to produce perceptually salient visualizations. Psychophysical experiments confirm a strong relationship between the expressive power of our nonphotorealistic properties and previous findings on the use of perceptual color and texture patterns for data display. Results from these studies are used to produce effective nonphotorealistic visualizations. We conclude by applying our techniques to a large, multidimensional weather dataset to demonstrate their viability in a practical, real-world setting.
TEXT MINING : PROMISES AND CHALLENGES
(1999)

Text mining, also known as knowledge discovery from text, and document information mining, refers to the process of extracting interesting patterns from very large text corpus for the purposes of discovering knowledge. Text mining is an interdisciplinary field involving information retrieval, text understanding, information extraction, clustering, categorization, visualization, database technology, machine learning, and data mining. Regarded by many as the next wave of knowledge discovery, text mining has a very high commercial value. This talk presents a general framework for text mining, consisting of two stages: text refining that transforms unstructured text documents into an intermediate form; and knowledge distill ation that deduces patterns or knowledge from the intermediate form. We then survey the state-of-the-art text mining approaches, products, and applications by aligning them based on the text refining and knowledge distill ation functions as well as the intermediate form that they adopt. In conclusion, we highlight the upcoming challenges of text mining and the opportunities it offers.
Soft-Switching Zeta–Flyback Converter With a Buck–Boost Type of Active Clamp
(2007)

This paper presents the system analysis, design consideration, and implementation of a soft-switching zeta-flyback converter to achieve zero-voltage switching (ZVS). In the proposed converter, the zeta and flyback topologies are adopted in the output side to achieve the following features: to share the power components in the transformer primary side, to achieve the partial magnetizing flux reset, and to share the output power. The buck-boost type of active clamp is connected in parallel with the primary side of the isolation transformer to recycle the energy stored in the leakage inductor of isolated transformer and to limit the peak voltage stress of switching devices due to the transformer leakage inductor when the main switch is turned off. The active clamp circuit can make the switching devices to turn on at ZVS. Experimental results taken from a laboratory prototype rated at 240 W, input voltage of 150 V, output voltage of 12 V, and switching frequency of 150 kHz are presented to demonstrate the converter performance. Based on the experimental results, the circuit efficiency is about 90.5% at rated output power, and the output voltage variation is about 1%.